1. Technical Field
The present disclosure relates to image processing, and more particularly to systems and methods for segmenting an object of interest from a medical image.
2. Discussion of Related Art
In recent years, medical imaging has experienced an explosive growth due to advances in imaging modalities such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound. Image segmentation plays an important role in computer-based medical applications for diagnosis and analysis of anatomical data by enabling automatic or semi-automatic extraction of an anatomical organ or object of interest from a dataset.
A segmentation technique to precisely and robustly label target tumor regions from three-dimensional (3-D) liver CT images, for example, may be useful in computer-aided cancer diagnosis and treatment planning such as follow-ups and computer-aided surgery planning. Liver tumors are prominent and outstanding in intensity distribution in a 3-D liver CT image. FIGS. 1A through 1D show examples of liver tumors in CT images. The two slices shown in FIGS. 1A and 1B, in which the liver tumors are indicated by arrows, are from the same liver CT scan. FIGS. 1C and 1D show an example of intensity distribution variation inside a liver tumor.
Liver tumor segmentation involves labeling all the voxels that belong to a target liver tumor in a 3-D liver CT image, such as specified by user interaction. The objectives in liver tumor segmentation include accurately locating the liver tumor regions in a 3-D liver CT image and producing consistent segmentation results that are robust to user interactions. It is not an easy task to precisely segment the liver tumor from the liver anatomy, except possibly in cases when the liver tumor is well isolated from adjacent anatomical structures.
A large number of liver tumors reside next to anatomical structures with similar intensity distribution in CT images, which may result in boundary blurriness between the liver tumors and the neighboring anatomy structures. The magnitude of gradient at the boundary points becomes much less prominent than that of the intensity variations within a liver tumor. It is very difficult to establish a criterion to identify the precise location of liver tumor boundary when such blurriness exists. Given that there are large variations in the intensity characteristics in liver CT images, neighboring anatomical structures, and intra-tumor intensity distribution, it is not easy to design a liver segmentation algorithm that produces a segmentation result that is robust to user interactions. Various liver segmentation algorithms have been proposed, some of which may perform reasonably well on liver CT images in cases when the liver is well-separated from neighboring anatomical structures and without significant boundary liver tumors. When livers are not well-separated from neighboring anatomical structures and/or there exist boundary tumors, extensive user interactions are needed.
Liver tumor segmentation may be handled as a modified region-growing problem, which in most cases does not produce an acceptable segmentation result, Moreover, this approach requires extensive user editing, which is not desirable in tumor segmentation. The graph cut algorithm has been tried for tumor segmentation. The graph cut algorithm uses seed points provided by a user to establish a graph representing the foreground and background relationship. It segments the foreground and background by calculating the min-cut. This technique may work reasonably well on some isolated tumors, but does not handle the blurred boundary cases. The random walk algorithm has been tried for tumor segmentation. This technique enables more informative constraints to be incorporated in the segmentation algorithm but does not handle the blurred boundary cases, and it requires a significant amount of user interactions, which is not desirable in 3-D tumor segmentation.